Each year, approximately 300,000 Americans die as a result of sudden cardiac death (SCD). However, the events and mechanisms associated with arrhythmias leading to SCD are still incompletely understood. An instability of cardiac electrical excitation waves that may result in malignant cardiac polymorphic arrhythmias and fibrillation is one of the most dangerous causes of sudden cardiac death. The first preventive step towards reducing mortality from SCD is to identify individuals with unstable propagation of electrical excitation in their hearts (the risk stratification).
The cardiovascular system responds to changes in physiological conditions primarily by adjustments of the heart rate, which can be evaluated from surface measurements of ECG R-R intervals, the time stretch between consecutive R-waves. Those R waves indicate the time-intervals between two consecutive heartbeats. Such adjustments normally occur simultaneously with corresponding changes in the duration of the ECG QT intervals, which characterize the duration of electrical excitation of cardiac muscle and represent the action potential duration averaged over a certain volume of cardiac muscle (FIG. 1).
Currently, practically all major non-invasive methods of assessing an individual's susceptibility to cardiac arrhythmias include some analysis of the QT and/or RR interval spatio-temporal distribution. Indeed, the QT interval dispersion is based on the assessment of myocardial repolarization inhomogeneity (M. Zabel et al., Electrocardiographic indexes of dispersion of ventricular repolarization: an isolated heart validation study, J. Am. Coll. Cardiol., 25:746–752 (1995); D. M. Mirvis, Spatial variation of QT intervals in normal persons and patients with acute myocardial infarction, J. Am. Coll. Cardiol., 5:625–631 (1985)). The T wave alternans method is concerned with alternating beat-to-beat variations of the morphology of the T wave, that marks the end of a repolarization period visualized on ECG as a QT interval (Kaplan et al. U.S. Pat. No. 4,732,157, 1988; Cohen et al. No. 4,802,491, 1989). A major approach that does not include a length of the QT interval, which reflects the duration of cardiac excitation, is the heart rate variability analysis (M. Malik, Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93:1043–1065 (1996)).
Recent advances in computer technology have led to improvements in automatic analysis of heart rate and QT interval variations. It is well known now that the QT interval's spatial variability (QT dispersion) observations performed separately or in combination with the heart rate (or RR interval) variability analysis provides a tool for the assessment of individual susceptibility to cardiac arrhythmias (B. Surawicz, J Cardiovasc Electrophysiol, 7:777–784 (1996)). Different types of assessment of the QT and some other interval variability, both spatial and temporal, were applied to assess the susceptibility to cardiac arrhythmias as described in U.S Patents by Chamoun U.S. Pat. No. 5,020,540, 1991; Wang U.S. Pat. No. 4,870,974, 1989; Kroll et al. U.S. Pat. No. 5,117,834, 1992; Henkin et al. U.S. Pat. No. 5,323,783, 1994; Xue et al. U.S. Pat. No. 5,792,065, 1998; Lander U.S. Pat. No. 5,827,195, 1998; Lander et al. U.S. Pat. No. 5,891,047, 1999; Hojum et al. U.S. Pat. No. 5,951,484, 1999.
Dror Sadeh and coworkers (N Engl J Med, 317:1501–1505, (1987); Comp. in Card. 125–127 (1987)) studied the dependence of the mean QT interval, <TQT>, on the mean RR interval, <TRR>, which they presented in the form of a power function, <TQT>=const·(<TRR>)β with a constant exponent β, similar to the classical Bazett equation (Bazett H. C., Heart, 7:353–370(1920)). They compared healthy infants and those who suffered from sudden infant death (SID) and found that the value of β in SID babies was only half of the β value in normal babies.
It was recently found that cardiac electrical instability can be also predicted by a combination of the QT—dispersion method observations with the ECG T-wave alternans method (Verrier et al., U.S. Pat. Nos. 5,560,370; 5,842,997; 5,921,940). This approach is somewhat useful in identifying and managing individuals at risk for sudden cardiac death. The authors report that QT interval dispersion is linked with risk for arrhythmias in patients with long QT syndrome. However, QT interval dispersion alone, without a simultaneous T wave alternans test, is said to be a less accurate predictor of cardiac electrical instability (U.S. Pat. No. 5,560,370 at column 6, lines 4–15).
Another application of the QT interval variability for prediction of sudden cardiac death is developed by J. Sarma (U.S. Pat. No. 5,419,338). He describes a method of an autonomic nervous system testing designed to evaluate the imbalances between both parasympathetic and sympathetic controls on the heart and, thus, to indicate a predisposition for sudden cardiac death.
The same author suggested that an autonomic nervous system testing procedure might be designed on the basis of the QT hysteresis (J. Sarma et al., PACE 10:485–491 (1988)). Hysteresis in the QT-interval during exercise and recovery was observed, and was attributed to sympatho-adrenal activity in the early post-exercise period. Such an activity was revealed in the course of QT interval adaptation to changes in the RR interval and was considered to be an indicator for sudden cardiac death.
It is a well-established physiological fact that the action potential duration (APD) of a cardiac cycle depends generally on the lengths of all preceding cardiac cycles. In order to simplify the matter physiologists use a specific experimental protocol (S1−S2 protocol) by which this multi-parametric dependence is reduced to the primary dependence on only two parameters, the period, Tc, of the conditioning pacing (S1, S1, . . . S1, separated by the same time interval Tc) with which the sample was consistently stimulated (trained) prior to the test stimulus S2, and the length, Tt, of the immediately preceding (testing) cardiac cycle, which is the time between the last stimulus S1 and the following test stimulus S2 (M. R. Boyett & B. R. Jewell, J Physiol, 285:359–380 (1978); V. Elharrar & B. Surawicz, Am J Physiol, 244:H782–H792 (1983)). The conditioning time or the number of conditioning stimuli, which are necessary for the consistency of the restitution results, constitutes another important and independent parameter of the medium (tissue). The physics related to excitable media points out two characteristics of the restitution process that are stability/instability indicators: the first is the slope of the restitution curve given by the dimensionless value of the partial derivative δTQT/δTt, and the second is the minimum training time or the characteristic transition time which is required for the wave to become periodic. The latter is similar to the conditioning time found empirically in the physiological experiments mentioned above. When this transition time is long, the medium is close to the unstable region, which closeness manifests itself by the presence of long-living oscillations of the APD and other characteristics of the wave. Such oscillations were observed in in vitro experiments (L. H. Frame & M. B. Simpson, Circulation, 78:1277–1287 (1988)) and in computer simulations using various models (Courtemanche et al, Phys Rev Lett, 14:2182–2185 (1993), SIAM J Appl Math, 56:119–142 (1996), Courtemanche, Chaos, 6:579–600 (1996), Y. Chernyak & J. Starobin, Crit. Rev. Biomed. Eng. 27:359 (1999), T. Hund & Y. Rudy, Am J Physiol, 279:H1869–H1879)). These fundamental physiological and physical facts constitute a general basis for the present invention.
The existing arrhythmia marker-type predictors mentioned above are accurate only under specific proarrhythmic physiological conditions, which may or may not occur in the cardiac muscle; and, therefore, they may falsely indicate an elevated arrhythmia risk (false positives) and an unnecessary electrophysiological (EP) study may ensue. The EP study is performed via cardiac catheterization, which is an invasive, expensive and somewhat hazardous procedure. Additionally, hand the existing methods possess insufficient specificity which deficiency results in missed proarrhythmic situations and lost opportunities for necessary remedial interventions. Hence, a sensitive and accurate non-invasive discrimination of proarrhythmic conditions in the heart is still a challenging diagnostic and signal-processing problem. A solution for this problem can be facilitated by the fact that computerized Holter monitors and similar devices for automatic ECG recording, its processing and obtaining QT and/or RR interval data sets are readily available and broadly accepted in clinical practice.
Accordingly, an object of the present invention is to provide a non-invasive technique for quantitatively assessing the risk of future cardiac arrhythmia in a patient.
Another object of the invention is to provide a non-invasive technique for quantitatively assessing the risk of future cardiac arrhythmia in a patient, which technique is not unduly uncomfortable or stressful for the patient.
Another object of the invention is to provide a non-invasive technique for quantitatively assessing the risk of future cardiac arrhythmia in a patient, which technique may be implemented with relatively simple equipment.
Still another object of the invention is to provide a non-invasive technique for quantitatively assessing the risk of future cardiac arrhythmia in a patient, which technique is sensitive to low risk levels of such arrhythmia.